Why we don't use importance sampling in tabular Q-Learning?
In Tabular Q-learning the update is as follows 

$$Q(s,a) = Q(s,a) + \alpha \left[R_{t+1} + \gamma \max_aQ(s',a) - Q(s,a) \right]\;.$$

Now, as we are interested in learning about the optimal policy, this would correspond to the $\max_aQ(s',a)$ term in the TD target because that is how the optimal policy chooses its actions - i.e. $\pi_*(a|s) = \arg\max_aQ_*(s,a)$, so eventually the greedy TD update would be greedy with respect to the optimal state-action value function due to the guaranteed convergence of Q-learning. 

The action $a$ in the update rule, i.e. the action we chose in state $s$ to receive the reward $R_{t+1}$, was chosen according to some non-optimal policy, e.g. $\epsilon$-greedy. However, as the $Q$ function is defined as the expected returns assuming we are in state $s$ and have taken action $a$ -- we thus don't need an importance sampling ratio for the $R_{t+1}$ term, even though it was generated from an action that the optimal policy might not have taken, because we are only updating the $Q$ function for state $s$ and action $a$, and by the definition of a $Q$ function it is assumed that we have taken action $a$ as we condition on this.